http://www.aryng.com
Predictive Analytics Keynote: When 3-pillar analytics approach is used, that's when one gets the most bang for the buck from Predictive Analytics or Any kind of analytics effort.

published:08 Nov 2011

views:615

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th AnnualResearchConference, titled "Reshaping Finance with AlternativeData".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack

published:06 Jul 2016

views:1074

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I learned this, how does it relate to what I know? Now does that matter and if so to who? One of the things that organizations have historically been doing is they take the transactions that happens, the piece of data that just happened in the enterprise, which is like a puzzle piece, its like a pixel, and they try to make a decision about whether it's good or bad by staring at the single puzzle piece. Well, there's a real limit to how smart you can be by staring at individual transactions. Contrast that with this notion of accumulating context. You get the next piece of data that happens in the enterprise, and you look at the puzzle and you look at the rest of the data, and now you figure out where it belongs. Sometimes the puzzle piece maybe connects two chucks of the puzzle that you hadn't anticipated. And it's only after you figure out where the puzzle piece goes and you can see the context, the surrounding things around it. That then is your best opportunity to say, Have I learned something that matters? So if you're in public safety you might be finding something that's a risk to the population. And if you're a bank or insurance company maybe it's helping you really better understand your customer.

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

published:05 Sep 2017

views:846

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

published:25 Apr 2017

views:403

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

published:25 Apr 2017

views:624

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

published:17 Dec 2018

views:18

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

published:11 Jul 2014

views:32829

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
ACM DL: http://dl.acm.org/citation.cfm?id=2858558
DOI: http://dx.doi.org/10.1145/2858036.2858558
------
https://chi2016.acm.org/wp/

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Putting Predictive Analytics in Business Context

http://www.aryng.com
Predictive Analytics Keynote: When 3-pillar analytics approach is used, that's when one gets the most bang for the buck from Predictive Analytics or Any kind of analytics effort.

36:58

Creating and Using Information through Predictive Analytics in Finance

Creating and Using Information through Predictive Analytics in Finance

Creating and Using Information through Predictive Analytics in Finance

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th AnnualResearchConference, titled "Reshaping Finance with AlternativeData".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack

1:31

Why Data Matters: Context Reveals Answers

Why Data Matters: Context Reveals Answers

Why Data Matters: Context Reveals Answers

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I learned this, how does it relate to what I know? Now does that matter and if so to who? One of the things that organizations have historically been doing is they take the transactions that happens, the piece of data that just happened in the enterprise, which is like a puzzle piece, its like a pixel, and they try to make a decision about whether it's good or bad by staring at the single puzzle piece. Well, there's a real limit to how smart you can be by staring at individual transactions. Contrast that with this notion of accumulating context. You get the next piece of data that happens in the enterprise, and you look at the puzzle and you look at the rest of the data, and now you figure out where it belongs. Sometimes the puzzle piece maybe connects two chucks of the puzzle that you hadn't anticipated. And it's only after you figure out where the puzzle piece goes and you can see the context, the surrounding things around it. That then is your best opportunity to say, Have I learned something that matters? So if you're in public safety you might be finding something that's a risk to the population. And if you're a bank or insurance company maybe it's helping you really better understand your customer.

Predictive Coding Models of Perception

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

1:01

What is predictive validity?

What is predictive validity?

What is predictive validity?

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

2:35

IBM Predictive Quality Maintenance Helps Predict the Future

IBM Predictive Quality Maintenance Helps Predict the Future

IBM Predictive Quality Maintenance Helps Predict the Future

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
ACM DL: http://dl.acm.org/citation.cfm?id=2858558
DOI: http://dx.doi.org/10.1145/2858036.2858558
------
https://chi2016.acm.org/wp/

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

1:52

Type Ahead, a continuous predictive text editor

Type Ahead, a continuous predictive text editor

Type Ahead, a continuous predictive text editor

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well as (b) a dictionary.
Several improvements are possible and easy. First, it could be better by checking a dictionary that was important to the current context. For instance, if you're writing a blog about JavaScript, the contexts would be JavaScript and web programming.
Second, the UI could be improved by offering a series of words in a drop-down list. Right now I only guess one word, but that UI would let me guess multiple words.
Furthermore, if it really understood 'context' well, things could get very interesting, potentially not just completing the current word, but possibly the one after that, and so on. Obviously there are limits to this, but what I'm thinking of here is something in the way that a Google search result shows you not just the current word you're typing, but the next most common matching words.

Putting Predictive Analytics in Business Context

http://www.aryng.com
Predictive Analytics Keynote: When 3-pillar analytics approach is used, that's when one gets the most bang for the buck from Predictive Analytics or Any kind of analytics effort.

published: 08 Nov 2011

Creating and Using Information through Predictive Analytics in Finance

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence ...

published: 06 Jul 2016

Why Data Matters: Context Reveals Answers

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I ...

Predictive Coding Models of Perception

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

published: 25 Apr 2017

What is predictive validity?

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

published: 17 Dec 2018

IBM Predictive Quality Maintenance Helps Predict the Future

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application,...

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

published: 25 Apr 2017

Type Ahead, a continuous predictive text editor

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well as (b) a dictionary.
Several improvements are possible and easy. First, it could be better by checking a dictionary that was important to the current context. For instance, if you're writing a blog about JavaScript, the contexts would be JavaScript and web programming.
Second, the UI could be improved by offering a series of words in a drop-down list. Right now I only guess one word, but that UI would let me guess multiple words.
Furthermore, if it really understood 'context' well, things could get very interesting, potentially not just completing the current word, but possibly the one after that, and so on. Obviously there ar...

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th AnnualResearchConference, titled "Reshaping Finance with AlternativeData".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th AnnualResearchConference, titled "Reshaping Finance with AlternativeData".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I learned this, how does it relate to what I know? Now does that matter and if so to who? One of the things that organizations have historically been doing is they take the transactions that happens, the piece of data that just happened in the enterprise, which is like a puzzle piece, its like a pixel, and they try to make a decision about whether it's good or bad by staring at the single puzzle piece. Well, there's a real limit to how smart you can be by staring at individual transactions. Contrast that with this notion of accumulating context. You get the next piece of data that happens in the enterprise, and you look at the puzzle and you look at the rest of the data, and now you figure out where it belongs. Sometimes the puzzle piece maybe connects two chucks of the puzzle that you hadn't anticipated. And it's only after you figure out where the puzzle piece goes and you can see the context, the surrounding things around it. That then is your best opportunity to say, Have I learned something that matters? So if you're in public safety you might be finding something that's a risk to the population. And if you're a bank or insurance company maybe it's helping you really better understand your customer.

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I learned this, how does it relate to what I know? Now does that matter and if so to who? One of the things that organizations have historically been doing is they take the transactions that happens, the piece of data that just happened in the enterprise, which is like a puzzle piece, its like a pixel, and they try to make a decision about whether it's good or bad by staring at the single puzzle piece. Well, there's a real limit to how smart you can be by staring at individual transactions. Contrast that with this notion of accumulating context. You get the next piece of data that happens in the enterprise, and you look at the puzzle and you look at the rest of the data, and now you figure out where it belongs. Sometimes the puzzle piece maybe connects two chucks of the puzzle that you hadn't anticipated. And it's only after you figure out where the puzzle piece goes and you can see the context, the surrounding things around it. That then is your best opportunity to say, Have I learned something that matters? So if you're in public safety you might be finding something that's a risk to the population. And if you're a bank or insurance company maybe it's helping you really better understand your customer.

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2e...

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

What is predictive validity?

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studi...

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

IBM Predictive Quality Maintenance Helps Predict the Future

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive Quali...

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
ACM DL: http://dl.acm.org/citation.cfm?id=2858558
DOI: http://dx.doi.org/10.1145/2858036.2858558
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https://chi2016.acm.org/wp/

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
ACM DL: http://dl.acm.org/citation.cfm?id=2858558
DOI: http://dx.doi.org/10.1145/2858036.2858558
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https://chi2016.acm.org/wp/

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

Type Ahead, a continuous predictive text editor

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well...

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well as (b) a dictionary.
Several improvements are possible and easy. First, it could be better by checking a dictionary that was important to the current context. For instance, if you're writing a blog about JavaScript, the contexts would be JavaScript and web programming.
Second, the UI could be improved by offering a series of words in a drop-down list. Right now I only guess one word, but that UI would let me guess multiple words.
Furthermore, if it really understood 'context' well, things could get very interesting, potentially not just completing the current word, but possibly the one after that, and so on. Obviously there are limits to this, but what I'm thinking of here is something in the way that a Google search result shows you not just the current word you're typing, but the next most common matching words.

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well as (b) a dictionary.
Several improvements are possible and easy. First, it could be better by checking a dictionary that was important to the current context. For instance, if you're writing a blog about JavaScript, the contexts would be JavaScript and web programming.
Second, the UI could be improved by offering a series of words in a drop-down list. Right now I only guess one word, but that UI would let me guess multiple words.
Furthermore, if it really understood 'context' well, things could get very interesting, potentially not just completing the current word, but possibly the one after that, and so on. Obviously there are limits to this, but what I'm thinking of here is something in the way that a Google search result shows you not just the current word you're typing, but the next most common matching words.

Creating and Using Information through Predictive Analytics in Finance

Slides available ► https://goo.gl/7Q9N8s
Full Event ► https://goo.gl/LvnmwY
Graham Giller - Office of ChiefData ScienceOfficer - J.P. Morgan
A review of key issues in performing predictive analytics on data in a financial context.
Predictive analytics is defined in terms of extracting information about future conditional distributions of data, and the importance of a leverageable information advantage is discussed including the measurement of value with respect to benchmarks and the importance of properly modelling causality.
Differences between financial market contexts and retail contexts in terms of the nature of the analysis and the nature of the data is examined, including non-normality, heteroskedasticity, the large degree of correlation found within the data and the existence of power-laws and genuine outliers.
Graham ends by reviewing the actual differences in what we can and should do with data that are the consequences of commodity storage and commodity computation in the big data era.
Session recorded June 16, 2016 at the RavenPack 4th AnnualResearchConference, titled "Reshaping Finance with AlternativeData".
Watch all sessions:
► https://goo.gl/3ij1Ev
Visit us at ►https://www.ravenpack.com/
Follow RavenPack on Twitter ► https://twitter.com/RavenPack

Why Data Matters: Context Reveals Answers

http://www.ibm.com/smarterplanet/us/en/index.html?cmp=agus_brsp2hp-20100301&cm=v&csr=hyp-context&cr=youtube_bc&ct=usbrb301&cm_mmc=agus_brsp2hp-20100301-usbrb301-_-v-_-hyp-context-_-youtube_bc When can a piece of data provide information that matters? IBM expert JeffJonas explains how context involves taking each transaction, deciding where it fits, and analyzing its surroundings in order to draw insight.
IBMer Jeff Jonas: The word context gets throw around a lot, but when I say, context, its looking at the things around something to better understand the thing. The question is: How can organizations do that in real time, as fast as they learn something? It's at that moment, if the organization wants to be really responsible and really competitive, they're going to say, Now that I learned this, how does it relate to what I know? Now does that matter and if so to who? One of the things that organizations have historically been doing is they take the transactions that happens, the piece of data that just happened in the enterprise, which is like a puzzle piece, its like a pixel, and they try to make a decision about whether it's good or bad by staring at the single puzzle piece. Well, there's a real limit to how smart you can be by staring at individual transactions. Contrast that with this notion of accumulating context. You get the next piece of data that happens in the enterprise, and you look at the puzzle and you look at the rest of the data, and now you figure out where it belongs. Sometimes the puzzle piece maybe connects two chucks of the puzzle that you hadn't anticipated. And it's only after you figure out where the puzzle piece goes and you can see the context, the surrounding things around it. That then is your best opportunity to say, Have I learned something that matters? So if you're in public safety you might be finding something that's a risk to the population. And if you're a bank or insurance company maybe it's helping you really better understand your customer.

Making Predictions with Data and Python : What Is Predictive Analytics? | packtpub.com

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2eZbdPP].
Explain to the viewer the definition of term Predictive Analytics and how it is different from other forms of making predictions.
• Explain what is a prediction in the context of the field of Predictive Analytics
• Explain the role of data in Predictive Analytics
• Give a concise and precise definition of the term Predictive Analytics
For the latest Big Data and Business Intelligence video tutorials, please visit
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This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

What is predictive validity?

This video introduces the concept of predictive validity, especially in the context of animal models, and how it relates to critical appraisal of research studies.
This video was developed with the help of a University of Sydney Educational Innovation Grant.

IBM Predictive Quality Maintenance Helps Predict the Future

http://ibm.co/1kUYk32
What if you could spot problems before they happen, so you could plan for asset failure and not simply react to it?
IBM Predictive QualityMaintenance, an integrated and pre-configured combination of proven IBM Software can find problems before they happen, helping organizations achieve a new level of productivity and cost savings.

When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive SystemsMatthew Kay, TaraKola, Jessica R Hullman, Sean A Munson
Abstract:
Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
ACM DL: http://dl.acm.org/citation.cfm?id=2858558
DOI: http://dx.doi.org/10.1145/2858036.2858558
------
https://chi2016.acm.org/wp/

This video illustrates the beam search based predictions for a sample user. Johns real timeline VS. our prediction.
Predictive analytics is a big thing to us. Our aim is to predict a series of events to explain an intent by analyzing behavioral patterns based on real-time sensor data to enable companies to be context aware and deliver timely and highly personalized experiences (sense & respond) but also to be one step ahead and proactively provide relevant recommendations by predicting context and preempting needs (predict & engage).

Type Ahead, a continuous predictive text editor

Type Ahead is a continuous predictive text editor. It always tries to help you auto-complete the current word by using words in (a) the current document as well as (b) a dictionary.
Several improvements are possible and easy. First, it could be better by checking a dictionary that was important to the current context. For instance, if you're writing a blog about JavaScript, the contexts would be JavaScript and web programming.
Second, the UI could be improved by offering a series of words in a drop-down list. Right now I only guess one word, but that UI would let me guess multiple words.
Furthermore, if it really understood 'context' well, things could get very interesting, potentially not just completing the current word, but possibly the one after that, and so on. Obviously there are limits to this, but what I'm thinking of here is something in the way that a Google search result shows you not just the current word you're typing, but the next most common matching words.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.